No effect of satellite tagging on survival, recruitment, longevity, productivity and social dominance of a raptor, and the provisioning and condition of its offspring
Bibliographic record
Abstract
Summary The deployment of electronic devices on animals is rapidly expanding and producing leapfrog advances in ecological knowledge. Even though their effects on the ecology and behaviour of the marked subjects are potentially important, <10% of the studies are accompanied by an evaluation of impact, and comprehensive, long‐term assessments have been few. Therefore, there is an urgent need to test for impacts, especially for tags that are heavy and deployed for long time periods, such as satellite transmitters. We marked 110 individuals of a medium‐sized, migratory raptor, the black kite Milvus migrans , with GPS satellite tags, representing about 4% of the body mass and attached as backpacks through a Teflon harness. Tagged individuals were compared to control animals of similar sex, age and breeding status for a large number of behavioural, condition‐related and ecological traits. Despite a sample size two‐ to threefold greater than most previous assessments that reported significant impacts, there was no detectable difference between tagged and control individuals in key vital rates such as survival probability, longevity, recruitment, age of first breeding, reproductive performance and timing of breeding. Tagged and untagged kites showed similar social dominance during fights over food and a similar capability to provision nestlings, which prevented carry‐over effects on the stress levels and condition of their offspring. Synthesis and applications . Radio‐marking studies are growing exponentially in the current ‘movement ecology era’, and impact assessments will be ever more important. In principle, tags of up to 4% mass‐load can be deployed without apparent harm on some avian soaring species, but impacts should be properly evaluated on a case‐by‐case basis. Resilient species for which impacts seem weak could be used as early warning systems for trials of new devices: if impacts are observed, they are likely to be even greater on more vulnerable species. Finally, individual fatalities caused by marking should be taken into serious account, but comprehensively evaluated in the light of broader population‐level impacts. Future initiatives to minimize tagging impacts could include more stringent licensing criteria enforcing attendance at training courses or incorporation of impact evaluations into study designs, increased availability of training courses for tagging, and enhanced sharing of information through blogs, workshops or specialized journal sections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".